The final capstone of the Knowledge Graphs track. Everything you've learned — scaling, temporal modeling, graph embeddings, and production GraphRAG — converges into two end-to-end platform designs: an enterprise compliance and provenance graph versus a large consumer GraphRAG product. You'll walk ingestion → extraction → entity resolution → storage and scale → hybrid retrieval → serving, with capacity planning, cost modeling, and a launch playbook for each.
This is the final capstone of the Knowledge Graphs track. The last four days each handed you one power tool — scaling (Day 1), temporal graphs (Day 2), graph embeddings (Day 3), and production GraphRAG (Day 4). Today we wire them into complete platforms by designing two real systems end-to-end and letting their differences teach the trade-offs.
A regulated bank needs a graph of who approved what, when, and on whose authority. Entities: people, accounts, transactions, controls, policies, documents. The killer requirement is provenance and auditability: every edge must carry its source, the time it was valid, and who asserted it. Auditors run multi-hop questions like "show every transaction over \$10k approved by someone who later failed a controls review." Scale is modest (~50M nodes, ~400M edges) but correctness, lineage, and bitemporal history are non-negotiable.
A consumer research assistant answers natural-language questions over ~80M documents by combining a knowledge graph with retrieval. Entities are extracted automatically from messy text at huge volume. The killer requirements are throughput, freshness, and answer quality at low latency — 3,000 QPS at peak, p95 under 900ms end-to-end, the graph growing by millions of nodes daily. Here a wrong-ish edge is tolerable; a slow or stale answer is not.
Every knowledge-graph platform — both of ours — is the same pipeline. Memorize it; it is the spine of the rest of this lesson:
The two case studies make the same decisions differently. That contrast is the lesson: there is no universal best architecture, only an architecture that fits a requirement profile.
Before any boxes-and-arrows, write the numbers down. The requirement table is the single most useful artifact in a design review:
| Dimension | ComplyGraph | AtlasRAG |
|---|---|---|
| Scale | 50M nodes / 400M edges | 1B+ nodes, +millions/day |
| Read pattern | Deep multi-hop audits, low QPS | Shallow hybrid retrieval, 3k QPS |
| Freshness | Hours acceptable | Minutes |
| Consistency | Strong, bitemporal, auditable | Eventual is fine |
| Failure cost | A wrong audit = regulatory fine | A stale answer = mild annoyance |
Notice nothing here is about technology yet. Pick the requirement profile first; the stack falls out of it.